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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2

Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations

Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter suchgeospatial videoson a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative,build-filter-observeparadigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3×, while maintaining up to 97.1% accuracy compared to unoptimized execution.

Computer Science↗

A comprehensive analytical model of rotorcraft aerodynamics and dynamics. Part 2: User's manual

The use of a computer program for a comprehensive analytical model of rotorcraft aerodynamics and dynamics is described. The program calculates the loads and motion of helicopter rotors and airframe. First the trim solution is obtained, then the flutter, flight dynamics, and/or transient behavior can be calculated. Either a new job can be initiated or further calculations can be performed for an old job.

Johnson, W.↗

Intelligent Agent Technology

Open Sesame! is the first commercial software product that learns user's behavior, and offers automation and coaching suggestions to the user. The neural learning module looks for repetitive patterns that have not been automated; when it finds one, it creates an observation and, upon approval, automates the task. The manufacturer, Charles River Analytics, credits Langley Research Center and Johnson Space Center Small Business Innovation Research grants and the time the president and vice president spent at the two centers in the 1970s as being essential to the development of their product line.

Source record↗

Energy I-Corps 2023: Distributed Wind Energy Toolkit

This presentation covers the purpose and scope of an Energy I-Corps project focused on refining a suite of analytical tools designed to support distributed wind deployment. These tools - the Distributed Generation Market Demand (dGen), Distributed Wind Tools Assessing Performance (TAP), and the Hybrid Optimization and Performance Platform (HOPP) - combine to provide users with the ability to simulate consumer purchasing behavior, accurately assess wind resource to better predict turbine performance, and effectively design and optimize hybrid systems that include distributed wind.

deployment↗

Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements

Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.

PMU, noise emulation, synthetic measurements, deep↗

Water recovery and management test support modeling for Space Station Freedom

The water-recovery and management (WRM) subsystem proposed for the Space Station Freedom program is outlined, and its computerized modeling and simulation based on a Computer Aided System Engineering and Analysis (CASE/A) program are discussed. A WRM test model consisting of a pretreated urine processing (TIMES), hygiene water processing (RO), RO brine processing using TIMES, and hygiene water storage is presented. Attention is drawn to such end-user equipment characteristics as the shower, dishwasher, clotheswasher, urine-collection facility, and handwash. The transient behavior of pretreated-urine, RO waste-hygiene, and RO brine tanks is assessed, as well as the total input/output to or from the system. The model is considered to be beneficial for pretest analytical predictions as a program cost-saving feature.

Mohamadinejad, Habib↗

A Privacy First Path Analysis using Clickstream Data

In the modern digital economy, data-driven decision making is crucial for effectively meeting the ever-evolving demands of consumer engagement and satisfaction. Clickstream data has become invaluable for understanding customer behavior, yet concerns over privacy and security persist, especially with some internet service providers profiting from its sale. This article introduces an innovative methodology that blends experiential learning with advanced cryptographic techniques, including differential privacy and graph analytics. The core objective of this methodology is to estimate Customer Lifetime Value (CLV) by analyzing clickstream data, achieving an average prediction accuracy of 92.4% in user engagement levels while ensuring user anonymity through Recency, Frequency, and Monetary (RFM) analysis. Our study introduces the concept of a “data depositor” and a privacy manager, employing the composition theorem to merge non-adaptive queries effectively. Privacy budgets (? = 1.0, d = 10-5), sensitivity-specific techniques, and data partitioning were applied. Randomization and noise addition protect data integrity, with special handling for categorical values. This approach, differing from prior studies, offers a 12.6% improvement in privacy-preserving targeting accuracy while maintaining strict confidentiality, presenting a novel path forward in data-driven decision-making.

Frequency and Monetary (RFM) analysis↗

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.

33 ADVANCED PROPULSION SYSTEMS↗

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗

Spaceflight Environmental-Telemetry Data for Biological Science

There is a critical need for better access and visualization of spaceflight environmental telemetry and mission hardware data from sensors including relative humidity, carbon dioxide, oxygen, radiation, airflow, temperature, acceleration, and acoustics. Under the stewardship of the Ames Life Sciences Data Archive (ALSDA) and GeneLab, an effort is underway to consolidate, normalize and provide accessibility of archived mission environmental data and hardware information, with the purpose of providing important context to biological data. This effort is necessary to provide scientific context of its impact upon biological and biomedical data from spaceflight missions and experiments (genomic, metagenomic, gene expression, proteomic, metabolomic, physiological, phenomics, behavioral; tabular, imaging, video). Environmental spaceflight data is derived from dozens of sources, with various formats, and in the past year a pipeline is in development to collect, curate and present this data efficiently. In the upcoming year, a new Data Visualization Portal will utilize the standardized pipeline data to provide easy user access to compare parameters and environmental conditions between missions, locations, subjects, and durations. Environmental and hardware data enables broad accessibility and analytics, without the need for advanced data informatic expertise. Familiarity with the capabilities and limitations of a variety of existing hardware/tools is a strength that could be applied to creation of improved hardware for future ecosystems on the Moon and Mars. The intention is to make biological and environmental telemetry data maximally open-access and FAIR (findable, accessible, interoperable, reusable) for data mining-informatic approaches to support knowledge discovery necessary for low Earth orbit, cis-Lunar, Mars transit, and Mars surface missions.

Danielle K. Lopez↗

The non-adiabatic nanoreactor: towards the automated discovery of photochemistry

The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules by probing the intersection seam between adiabatic electronic states with hyper-real metadynamics, sampling the branching plane for relevant conical intersections, and performing seam-constrained path searches. We illustrate the effectiveness of the nonadiabatic nanoreactor by applying it to benzene, a molecule with rich photochemistry and a wide array of photochemical products. Our study confirms the existence of several types of S 0 /S 1 and S 1 /S 2 conical intersections which mediate access to a variety of ground state stationary points. We elucidate the connections between conical intersection energy/topography and the resulting photoproduct distribution, which changes smoothly along seam space segments. The exploration is performed with minimal user input, and the protocol requires no previous knowledge of the photochemical behavior of a target molecule. We demonstrate that the nonadiabatic nanoreactor is a valuable tool for the automated exploration of photochemical reactions and their mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Industrial Productivity

NASTRAN is an offshoot of the computer-design technique used in construction of airplanes and spacecraft. [n this technique engineers create a mathematical model of the aeronautical or space vehicle and "fly" it on the ground by means of computer simulation. The technique enables them to study performance and structural behavior of a number of different designs before settling on the final configuration and proceeding with construction. From this base of aerospace experience, NASA-Goddard developed the NASTRAN general purpose computer program, which offers an exceptionally wide range of analytic capability with regard to structures. NASTRAN has been applied to autos, trucks, railroad cars, ships, nuclear power reactors, steam turbines, bridges, and office buildings. NASA-Langley provides program maintenance services regarded as vital by many NASTRAN users. NASTRAN is essentially a predictive tool. It takes an electronic look at a computerire$.dedgn and reports how the structure will react under a great many different conditions. It can, for example, note areas where high stress levels will occur-potential failure points that need strengthening. Conversely, it can identify over-designed areas where weight and material might be saved safely. NASTRAN can tell how pipes stand up under strong fluid flow, how metals are affected by high temperatures, how a building will fare in an earthquake or how powerful winds will cause a bridge to oscillate. NASTRAN analysis is quick and inexpensive. It minimizes trial-and-error in the design process and makes possible better, safe, lighter structures affording large-scale savings in development time and materials. Some examples of the broad utility NASTRAN is finding among industrial firms are shown on these pages.

Source record↗

Web-based Visualization and Analytics of Petascale Data: Equity as a Tide that Lifts All Boats

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hid-ing the complexities of dealing with file systems or cloud services.We also optimize network utilization while streaming from petas-cale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any inter-net connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA’s use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution.These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Data visualization↗

NRAP-Open-IAM Analytical Reservoir Model: Development and Testing

Geological carbon sequestration (GCS) is a key technology for reducing global carbon dioxide (CO 2 ) emissions. Over the last decade, the U.S. Department of Energy has invested in understanding the science base, developing practical implementation methods, and demonstrating secure GCS technologies to mitigate the environmental impacts associated with the atmospheric release of CO 2 . As part of the National Risk Assessment Partnership, a systems-level risk assessment tool, called the NRAP-Open-IAM, has been developed to conduct risk assessment and enable safe operations at a GCS site. The current NRAP-Open-IAM contains a simple reservoir model component that calculates the evolution of CO 2 saturation and fluid pressure in a storage reservoir during CO 2 injection operations. This report presents the development and testing of a new analytical reservoir reduced-order model (ROM), which is extended from an existing semi-analytical model for estimation of CO 2 and brine leakage along legacy wells, and enhances the capability of the NRAP-Open-IAM to simulate more types of reservoir conditions. The developed model is validated against three reference studies, and the results indicate that the new ROM predicts the behavior of the two-phase fluids (brine and injected CO 2 ) well and is applicable to different reservoir simulation boundary conditions (i.e., constant pressure boundary and infinite-acting boundary) without a priori user specification of the boundary type. Sensitivity analysis for a set of model parameters is performed using 4,000 synthetic cases prepared via a fully automated process and using machine-learning-based feature selection. The stochastic analysis identifies gravitational number (i.e., ratio of gravitational forces to viscous force) and distance between the injection well and observation location as the most impactful parameters for matching the pressure and CO 2 saturation, respectively, between the numerical simulations and the ROM. This report details the possible ROM uncertainties and serves as a guide for users to understand the use and limitations of this ROM. The code implementation of the model will be released as a module within the NRAP-Open-IAM.

54 ENVIRONMENTAL SCIENCES↗

A method for characterization of multiple dynamic constitutive parameters of FRCs

We propose a method to measure multiple dynamic material constitutive parameters of unidirectional fiber reinforced composites (FRCs) in a single experiment. Dynamic short-beam shear (DSBS) experiments were performed on a modified Kolsky compression bar, with integration of high-speed imaging and digital image correlation (DIC). The unidirectional FRCs investigated were S-2 glass fiber reinforced matrix of TGDDM-Jeffamine® D230 with monoamine functionalized partially reacted substructures (mPRS) and commercially available SC-15. Analytical solutions of normal and shear strains of a composite beam were derived based on Timoshenko beam theory, assuming material to be transversely isotropic and have different moduli in tension and compression in each principle material orientation. Tensile and compressive moduli were inversely computed through monitoring normal strain slope when specimen was constantly loaded at a speed of ~7.3 m/s within a small deflection. Non-linear shear stress-strain behavior of the composite was described via Ramberg–Osgood equation. Finite element (FE) analysis was conducted in ABAQUS, simultaneously defining via user subroutine UMAT the transverse isotropy of material, bi-modulus constitutive model, and non-linear shear stress-strain relation. The method proposed in this work was validated by comparing strain distributions computed by FE model and DIC measurements. Comparing with traditional dynamic tensile, compressive, and shear experiments on FRCs, this method significantly simplifies the specimen preparation and design of complicated gripping fixtures for multiple experiments. Here, systematic errors resulting from variations of specimen geometry and dimension, loading direction, and instrumentation are reduced, thereby providing compatible data for numerical studies on impact behavior of composites.

36 MATERIALS SCIENCE↗

A simplified computer program for the prediction of the linear stability behavior of liquid propellant combustors

A program for predicting the linear stability of liquid propellant rocket engines is presented. The underlying model assumptions and analytical steps necessary for understanding the program and its input and output are also given. The rocket engine is modeled as a right circular cylinder with an injector with a concentrated combustion zone, a nozzle, finite mean flow, and an acoustic admittance, or the sensitive time lag theory. The resulting partial differential equations are combined into two governing integral equations by the use of the Green's function method. These equations are solved using a successive approximation technique for the small amplitude (linear) case. The computational method used as well as the various user options available are discussed. Finally, a flow diagram, sample input and output for a typical application and a complete program listing for program MODULE are presented.

Mitchell, C. E.↗

LS-DYNA Implementation of Polymer Matrix Composite Model Under High Strain Rate Impact

A recently developed constitutive model is implemented into LS-DYNA as a user defined material model (UMAT) to characterize the nonlinear strain rate dependent behavior of polymers. By utilizing this model within a micromechanics technique based on a laminate analogy, an algorithm to analyze the strain rate dependent, nonlinear deformation of a fiber reinforced polymer matrix composite is then developed as a UMAT to simulate the response of these composites under high strain rate impact. The models are designed for shell elements in order to ensure computational efficiency. Experimental and numerical stress-strain curves are compared for two representative polymers and a representative polymer matrix composite, with the analytical model predicting the experimental response reasonably well.

Zheng, Xia-Hua↗

Foundations of measurement and instrumentation

The user of instrumentation has provided an understanding of the factors that influence instrument performance, selection, and application, and of the methods of interpreting and presenting the results of measurements. Such understanding is prerequisite to the successful attainment of the best compromise among reliability, accuracy, speed, cost, and importance of the measurement operation in achieving the ultimate goal of a project. Some subjects covered are dimensions; units; sources of measurement error; methods of describing and estimating accuracy; deduction and presentation of results through empirical equations, including the method of least squares; experimental and analytical methods of determining the static and dynamic behavior of instrumentation systems, including the use of analogs.

Warshawsky, Isidore↗